Prediction Model for Familial Aggregated HBV‐Associated Hepatocellular Carcinoma Based on Serum Biomarkers
ABSTRACT Background Accurate assessment of the risk of familial aggregated hepatitis B virus (HBV)‐associated hepatocellular carcinoma (HCC) and regular surveillance for these patients at high risk may be valuable to reduce the occurrence and improve the prognosis of HCC. Aim This study aimed to dev...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Cancer Reports |
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| Online Access: | https://doi.org/10.1002/cnr2.70253 |
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| author | Linmei Zhong Guole Nie Qiaoping Wu Honglong Zhang Haiping Wang Jun Yan |
| author_facet | Linmei Zhong Guole Nie Qiaoping Wu Honglong Zhang Haiping Wang Jun Yan |
| author_sort | Linmei Zhong |
| collection | DOAJ |
| description | ABSTRACT Background Accurate assessment of the risk of familial aggregated hepatitis B virus (HBV)‐associated hepatocellular carcinoma (HCC) and regular surveillance for these patients at high risk may be valuable to reduce the occurrence and improve the prognosis of HCC. Aim This study aimed to develop a simple and reliable prediction model for the risk of HCC in these patients. Methods and Results This study analyzed clinical laboratory results from a database of 1285 patients with familial aggregated HBV who attended the First Hospital of Lanzhou University from January 2010 to December 2019. Univariate and multivariate logistic regression (LR) analysis showed that hemoglobin (Hb), neutrophil percentage (NP), total protein (TP), glutamyl transpeptidase (GGT), alglucosidase alfa (AFU), aspartate aminotransferase (AST) to Alanine aminotransferase (ALT) ratio (AAR), and alpha‐fetoprotein (AFP) were identified to be independent risk factors for HBV‐associated HCC. Prediction models were developed using a multivariate LR model, classification and regression tree, Native Bayes, Bagged tree, AdaBoost, and random forest. We used a multivariate LR model as a benchmark for performance assessment (AUC = 0.737). The results showed that the Native Bayes model had an AUC of 0.749, which was better than that of the other models. Conclusion Finally, the Native Bayes model demonstrated better predictive performance for HCC, which helped in the clinical decision‐making and identification of HCC high‐risk groups. |
| format | Article |
| id | doaj-art-db1ad25c4eba41fe84eff44946acbb09 |
| institution | Kabale University |
| issn | 2573-8348 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Reports |
| spelling | doaj-art-db1ad25c4eba41fe84eff44946acbb092025-08-20T03:27:01ZengWileyCancer Reports2573-83482025-06-0186n/an/a10.1002/cnr2.70253Prediction Model for Familial Aggregated HBV‐Associated Hepatocellular Carcinoma Based on Serum BiomarkersLinmei Zhong0Guole Nie1Qiaoping Wu2Honglong Zhang3Haiping Wang4Jun Yan5Postgraduate Training Base Alliance, Wenzhou Medical University Wenzhou Zhejiang People's Republic of ChinaDepartment of Colorectal Hernia Surgery Binzhou Medical University Hospital Binzhou Shandong People's Republic of ChinaDepartment of Pediatrics The First Affiliated Hospital of Fujian Medical University Fuzhou Fujian People's Republic of ChinaThe First School of Clinical Medicine, Lanzhou University Lanzhou Gansu People's Republic of ChinaThe First School of Clinical Medicine, Lanzhou University Lanzhou Gansu People's Republic of ChinaThe First School of Clinical Medicine, Lanzhou University Lanzhou Gansu People's Republic of ChinaABSTRACT Background Accurate assessment of the risk of familial aggregated hepatitis B virus (HBV)‐associated hepatocellular carcinoma (HCC) and regular surveillance for these patients at high risk may be valuable to reduce the occurrence and improve the prognosis of HCC. Aim This study aimed to develop a simple and reliable prediction model for the risk of HCC in these patients. Methods and Results This study analyzed clinical laboratory results from a database of 1285 patients with familial aggregated HBV who attended the First Hospital of Lanzhou University from January 2010 to December 2019. Univariate and multivariate logistic regression (LR) analysis showed that hemoglobin (Hb), neutrophil percentage (NP), total protein (TP), glutamyl transpeptidase (GGT), alglucosidase alfa (AFU), aspartate aminotransferase (AST) to Alanine aminotransferase (ALT) ratio (AAR), and alpha‐fetoprotein (AFP) were identified to be independent risk factors for HBV‐associated HCC. Prediction models were developed using a multivariate LR model, classification and regression tree, Native Bayes, Bagged tree, AdaBoost, and random forest. We used a multivariate LR model as a benchmark for performance assessment (AUC = 0.737). The results showed that the Native Bayes model had an AUC of 0.749, which was better than that of the other models. Conclusion Finally, the Native Bayes model demonstrated better predictive performance for HCC, which helped in the clinical decision‐making and identification of HCC high‐risk groups.https://doi.org/10.1002/cnr2.70253familial aggregated HBVhepatocellular carcinomamachine learningrisk prediction |
| spellingShingle | Linmei Zhong Guole Nie Qiaoping Wu Honglong Zhang Haiping Wang Jun Yan Prediction Model for Familial Aggregated HBV‐Associated Hepatocellular Carcinoma Based on Serum Biomarkers Cancer Reports familial aggregated HBV hepatocellular carcinoma machine learning risk prediction |
| title | Prediction Model for Familial Aggregated HBV‐Associated Hepatocellular Carcinoma Based on Serum Biomarkers |
| title_full | Prediction Model for Familial Aggregated HBV‐Associated Hepatocellular Carcinoma Based on Serum Biomarkers |
| title_fullStr | Prediction Model for Familial Aggregated HBV‐Associated Hepatocellular Carcinoma Based on Serum Biomarkers |
| title_full_unstemmed | Prediction Model for Familial Aggregated HBV‐Associated Hepatocellular Carcinoma Based on Serum Biomarkers |
| title_short | Prediction Model for Familial Aggregated HBV‐Associated Hepatocellular Carcinoma Based on Serum Biomarkers |
| title_sort | prediction model for familial aggregated hbv associated hepatocellular carcinoma based on serum biomarkers |
| topic | familial aggregated HBV hepatocellular carcinoma machine learning risk prediction |
| url | https://doi.org/10.1002/cnr2.70253 |
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